CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Learning Consistent Feature Representation for Cross-Modal Multimedia Retrieval
Kang, Cuicui; Xiang, Shiming; Liao, Shengcai; Xu, Changsheng; Pan, Chunhong
AbstractThe cross-modal feature matching has gained much attention in recent years, which has many practical applications, such as the text-to-image retrieval. The most difficult problem of cross-modal matching is how to eliminate the heterogeneity between modalities. The existing methods (e.g., CCA and PLS) try to learn a common latent subspace, where the heterogeneity between two modalities is minimized so that cross-matching is possible. However, most of these methods require fully paired samples and suffer difficulties when dealing with unpaired data. Besides, utilizing the class label information has been found as a good way to reduce the semantic gap between the low-level image features and high-level document descriptions. Considering this, we propose a novel and effective supervised algorithm, which can also deal with the unpaired data. In the proposed formulation, the basis matrices of different modalities are jointly learned based on the training samples. Moreover, a local group-based priori is proposed in the formulation to make a better use of popular block based features (e.g., HOG and GIST). Extensive experiments are conducted on four public databases: Pascal VOC2007, LabelMe, Wikipedia, and NUS-WIDE. We also evaluated the proposed algorithm with unpaired data. By comparing with existing state-of-the-art algorithms, the results show that the proposed algorithm is more robust and achieves the best performance, which outperforms the second best algorithm by about 5% on both the Pascal VOC2007 and NUS-WIDE databases.
KeywordCross-modal Matching Documents And Images Multimedia Retrieval
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS IDWOS:000351585700009
Citation statistics
Document Type期刊论文
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Kang, Cuicui,Xiang, Shiming,Liao, Shengcai,et al. Learning Consistent Feature Representation for Cross-Modal Multimedia Retrieval[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2015,17(3):370-381.
APA Kang, Cuicui,Xiang, Shiming,Liao, Shengcai,Xu, Changsheng,&Pan, Chunhong.(2015).Learning Consistent Feature Representation for Cross-Modal Multimedia Retrieval.IEEE TRANSACTIONS ON MULTIMEDIA,17(3),370-381.
MLA Kang, Cuicui,et al."Learning Consistent Feature Representation for Cross-Modal Multimedia Retrieval".IEEE TRANSACTIONS ON MULTIMEDIA 17.3(2015):370-381.
Files in This Item: Download All
File Name/Size DocType Version Access License
2015_康翠翠_TMM.PDF(2326KB)期刊论文出版稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Kang, Cuicui]'s Articles
[Xiang, Shiming]'s Articles
[Liao, Shengcai]'s Articles
Baidu academic
Similar articles in Baidu academic
[Kang, Cuicui]'s Articles
[Xiang, Shiming]'s Articles
[Liao, Shengcai]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Kang, Cuicui]'s Articles
[Xiang, Shiming]'s Articles
[Liao, Shengcai]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 2015_康翠翠_TMM.PDF
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.